arXiv:2607.08408v1 Announce Type: cross Abstract: Gaussian splatting is the current state-of-the-art for dense, deformable 3D anatomy reconstruction in robot-assisted minimally invasive surgery (RAMIS); however, most pipelines are offline and depend on accurate camera trajectory priors (often from robotic kinematics), limiting applicability when priors are missing or noisy. To address these limitations, we propose Track2Map, an online 3D Gaussian Splatting pipeline that jointly optimizes camera trajectory and 3D deformable scene representation directly from surgical video. Track2Map is therefo
Rapid advancements in 3D reconstruction and AI are enabling more robust real-time applications like deformable SLAM, pushing the boundaries for robotic assistance in complex environments.
This development enhances the autonomy and precision of robotic surgery by addressing current limitations in 3D scene reconstruction and camera trajectory optimization during procedures.
Robotic surgery systems can now more accurately map and track deformable anatomy online, reducing reliance on external priors and expanding applicability to a wider range of surgical scenarios.
- · Robotic surgery companies
- · Medical AI developers
- · Patients undergoing minimally invasive surgery
- · Healthcare providers
- · Systems heavily reliant on pre-operative scans
- · Manual surgical techniques in complex procedures
More precise and safer robot-assisted minimally invasive surgeries become possible.
Reduced physician fatigue and improved surgical outcomes due to enhanced robotic capabilities.
Accelerated development of fully autonomous surgical robots as real-time feedback loops become more sophisticated.
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Read at arXiv cs.AI